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Machine learning methods provide a powerful basis for developing flexible, non-parametric models of complex phenomena and have demonstrated strong predictive capabilities across many areas of the physical sciences generally and the earth sciences specifically. While machine learning methods have been demonstrated to be flexible predictive tools capable of integrating diverse data streams, they present significant challenges in terms of interpretability and generalizability. This is especially true in the context of ecohydrological or biophysical systems, where the objective is often to develop a better understanding of the underlying system rather than exclusively improve predictive performance. There is a growing recognition that interpretability, physical consistency, and data complexity are key challenges in the successful adoption of machine learning methodologies. Here we evaluate the application of machine learning methods to produce models for land-atmosphere water vapor exchange across a set of diverse agricultural systems. Specific focus is placed on the use of environmental and proximal sensing information to develop simple and effective models of evapotranspiration using both machine learning and hybrid modeling approaches that leverage the advantages of machine learning and biophysical simulation. Emphasis is placed on parsimonious model development and interpretability of model performance. (2026-03-13)
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